Machine learning: What developers and business analysts need to know

There is more to a successful application of machine learning than data science

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TensorFlow makes an abstraction between the model (called an estimator) and the algorithm (called an optimizer), allowing a user to select from multiple algorithms when training a model. For example, a specialist could write a supervised learning model using simple linear regression as the algorithm, and then compare its accuracy against a deep neural network algorithm.

History repeats

The rise of machine learning has striking parallels to the rise of the Internet. For decades both were studied by university researchers and saw limited commercial use. The Internet, based on a network that was turned on in 1969, had a coming of age in the 90’s that disrupted industries with incumbents who reacted slowly until their businesses were marginalized. Now many of the same companies that rose to prominence with the Internet are leading the adoption of machine learning, while incumbents try to understand its significance and extract value from their data science investments.

Any software project that gives an organization insight into its business requires the close participation between business users and people with skills to translate business requirements into code. Most organizations with software investments are familiar with this paradigm. A key difference is that while machine learning requires a definition of the problem, its purpose is to find a solution.

The widespread adoption of machine learning requires at least a black-box level of understanding from business analysts and software developers as they engage with data science teams. It also requires business leaders with a vision of how they will get value from using machine learning to solve problems previously addressed with carefully defined rules. Making successful use of machine learning does not require most people involved to understand the details of how machine learning works. But they need to understand enough to ask the right questions from the data science experts.

Chad Juliano is a senior solutions architect for Kinetica. Previously, Chad was a senior principal consultant for Oracle. Prior to Oracle, he worked as a software engineer at Quorum Business Solutions. Chad also had prior experience at Portal Software. Chad earned a double major in electrical engineering and math from Southern Methodist University in Dallas.  

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